Wavelet-based feature extraction using continuous wavelet transform for power quality disturbances classification
DOI:
https://doi.org/10.33837/msj.v8i1.1732Keywords:
power quality, disturbance classification, voltage events, feature extraction, Continuous wavelet transformAbstract
Wavelet analysis is well established as an effective signal processing tool for detecting and analysing power quality disturbances. Several studies have employed the fast wavelet transform to construct feature vectors used in the power quality analysis. This paper presents a new wavelet-based feature extraction method for power quality disturbances classification using the continuous wavelet transform. A set of small scales is used for this purpose, and only the energy of each decomposed signal is used to construct the feature vector for classification purposes. The chosen scales are those representing the natural frequency and its harmonics. The proposed method is applied to voltage event signals generated by a frequently used mathematical model in the specialized literature for power quality disturbances analysis. Experiments with both clean and noisy signals demonstrate that classification accuracy approaching 100% can be achieved using only two scales of the continuous wavelet transform. Some wavelets are tested, and those with an appropriate number of vanishing moments presents better results. The results are compared with those obtained from other wavelet-based methods employing the fast wavelet transform. The effectiveness of the proposed wavelet-based feature extraction method is evaluated with several classifiers, consistently yielding high accuracy.
References
Abdel-Galil, T. K. A., Kamel, M., Youssef, A. M., El-Saadany, E. F. E., & Salama, M. M. A. (2004). Power quality disturbance classification using the inductive inference approach. IEEE Transactions on Power Delivery, 19(4), 1812–1818.
Abry, P. (1997). Ondelettes et turbulences: multirésolutions, algorithmes de décomposition, invariance d’échelle et signaux de pression. Diderot Multimédia.
Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37–66.
Akmaz, D. (2022). A new signal processing approach for classification of power quality disturbances. Digital Signal Processing, 130, 103701.
Angrisani, L., Daponte, P., & D’Apuzzo. (2001). Wavelet network-based detection and classification of transients. IEEE Transactions on Instrumentation and Measurement, 50(5), 1425–1435.
Barros, J., Diego, R. I., & de Apráiz, M. (2012). Applications of wavelets in electric power quality: Voltage events. Electric Power Systems Research, 88, 130–136.
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Cai, J., Zhang, K., & Jiang, H. (2023). Power quality disturbance classification based on parallel fusion of CNN and GRU. Energies, 16(10), 4029.
Caicedo, J. E., Agudelo-Martínez, D., Rivas-Trujillo, E., & Meyer, J. (2023). A systematic review of real-time detection and classification of power quality disturbances. Protection and Control of Modern Power Systems, 8, 1–37.
Calderon, A. P. (1964). Intermediate spaces and interpolation: The complex method. Studia Mathematica, 24, 113–190.
Chawda, G. S., Shaik, A. G., Shaik, M., Padmanaban, S., Holm-Nielsen, J. B., Mahela, O. P., & Kaliannan, P. (2020). Comprehensive review on detection and classification of power quality disturbances in utility grid with renewable energy penetration. IEEE Access, 8, 146807–146830.
Cleary, J. G., & Trigg, L. E. (1995). K*: An instance-based learner using an entropic distance measure. In Machine Learning Proceedings 1995 (pp. 108–114). Elsevier.
Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 41(7), 909–996.
Decanini, J. G., Tonelli-Neto, M. S., Malange, F. C., & Minussi, C. R. (2011). Detection and classification of voltage disturbances using a fuzzy-ARTMAP-wavelet network. Electric Power Systems Research, 81(10), 2057–2065.
Grossmann, A., & Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis, 15(4), 723–736.
Haykin, S. (1998). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.
He, H., & Starzyk, J. A. (2006). A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Transactions on Power Delivery, 21(1), 286–295.
Eristi, H., Uçar, A., & Demir, Y. (2010). Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines. Electric Power Systems Research, 80(7), 743–752.
Huang, S. J., Hsieh, C. T., & Huang, C. L. (1998). Application of wavelets to classify power system disturbances. Electric Power Systems Research, 47(2), 87–93.
IEEE Standards Association. (2009). IEEE recommended practice for monitoring electric power quality (IEEE Std 1159-2009). IEEE, Inc., NY, USA.
Jiang, J., Wu, H., Zhong, C., Cai, Y., & Song, H. (2024). A novel methodology for microgrid power quality disturbance classification using URPM-CWT and multi-channel feature fusion. IEEE Access, 12, 35597–35611.
Jiang, Z., Wang, Y., Li, Y., & Cao, H. (2024). A new method for recognition and classification of power quality disturbances based on IAST and RF. Electric Power Systems Research, 226, 109939. https://doi.org/10.1016/j.epsr.2024.109939
Khetarpal, P., Nagpal, N., Alhelou, H. H., Siano, P., & Al-Numay, M. (2024). Noisy and non-stationary power quality disturbance classification based on adaptive segmentation empirical wavelet transform and support vector machine. Computers and Electrical Engineering, 118, 109346.
Koski, T., & Noble, J. (2011). Bayesian networks: An introduction. Wiley.
Lin, C. H., & Wang, C. H. (2006). Adaptive wavelet networks for power quality detection and discrimination in a power system. IEEE Transactions on Power Delivery, 21(3), 1106–1113.
Mallat, S. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.
Markovska, M., & Taškovski, D. (2018). Efficient feature extraction and classification of power quality disturbances. Journal of Electrical Engineering and Information Technologies, 3(1), 13–20.
Meyer, Y. (1993). Wavelets: Algorithms and applications. Society for Industrial and Applied Mathematics.
Pillay, P., & Bhattacharjee, A. (1996). Application of wavelets to model short-term power system disturbances. IEEE Transactions on Power Systems, 11(4), 2031–2037.
Saini, M. K., & Beniwal, R. K. (2017). Optimum fractionally delayed wavelet design for PQ event detection and classification. International Transactions on Electrical Energy Systems, 1–15.
Salles, R. S., & Ribeiro, P. F. (2023). The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification. Electric Power Systems Research, 214, 108834.
Samanta, I. S., Rout, P. K., Swain, K., Cherukuri, M., & Mishra, S. (2022). Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine. Computers and Electrical Engineering, 100, 107926.
Santoso, S., Powers, E. J., & Grady, W. M. (1994). Electric power quality disturbance detection using wavelet transform analysis. In Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis.
Santoso, S., Powers, E. J., Grady, W. M., & Hofmann, P. (1996). Power quality assessment via wavelet transform analysis. IEEE Transactions on Power Delivery, 11(2), 924–930.
Sengur, A., Turkoglu, I., & Ince, M. C. (2007). Wavelet packet neural networks for texture classification. Expert Systems with Applications, 32(2), 527–533.
Tse, N. C. F., Zhou, L., & Lai, L. L. (2010). Wavelet-based algorithm for power quality analysis. European Transactions on Electrical Power, 20(7), 952–964.
Uyara, M., Yildirim, S., & Gencoglu, M. (2008). An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electric Power Systems Research, 78(10), 1747–1755.
Zhang, Y., Zhang, Y., & Zhou, X. (2022). Classification of power quality disturbances using visual attention mechanism and feed-forward neural network. Measurement, 188, 110390.
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